{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Forecasting Models: Time series with different lengths and different exogenous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When faced with a multi-series forecasting problem, it is common for the series to have varying lengths due to differences in the starting times of data recording. To address this scenario, the [ForecasterRecursiveMultiSeries](../user_guides/independent-multi-time-series-forecasting.html) class supports simultaneous modeling of multiple time series, even when they differ in length and use distinct exogenous variables. The various input formats can be combined, provided that the following requirements are met:\n",
    "\n",
    "| Series type            | Exog type                                        | Index requirements                     |\n",
    "|:----------------------:|:------------------------------------------------:|:---------------------------------------|\n",
    "| `Wide DataFrame`       | `Wide DataFrame`, `MultiIndex DataFrame`, `dict` | `pandas DatetimeIndex` or `RangeIndex` |\n",
    "| `MultiIndex DataFrame` | `Wide DataFrame`, `MultiIndex DataFrame`, `dict` | `pandas DatetimeIndex`                 |\n",
    "| `dict`                 | `Wide DataFrame`, `MultiIndex DataFrame`, `dict` | `pandas DatetimeIndex` or `RangeIndex` |\n",
    "\n",
    "Regarding the presence of missing values in the series, the `ForecasterRecursiveMultiSeries` class can handle all scenarios:\n",
    "\n",
    "| Series values                            | Allowed |\n",
    "|:----------------------------------------:|:-------:|\n",
    "| `[NaN, NaN, NaN, NaN, 4, 5, 6, 7, 8, 9]` |✔️       |\n",
    "| `[0, 1, 2, 3, 4, 5, 6, 7, 8, NaN]`       |✔️       |\n",
    "| `[0, 1, 2, 3, 4, NaN, 6, 7, 8, 9]`       |✔️       |\n",
    "| `[NaN, NaN, 2, 3, 4, NaN, 6, 7, 8, 9]`   |✔️       |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "API Reference <a href=\"../api/forecasterrecursivemultiseries.html\">ForecasterRecursiveMultiSeries</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint\n",
    "from lightgbm import LGBMRegressor\n",
    "from skforecast.plot import set_dark_theme\n",
    "from skforecast.preprocessing import (\n",
    "    reshape_series_long_to_dict, \n",
    "    reshape_exog_long_to_dict, \n",
    "    RollingFeatures\n",
    ")\n",
    "from skforecast.recursive import ForecasterRecursiveMultiSeries\n",
    "from skforecast.model_selection import (\n",
    "    TimeSeriesFold,\n",
    "    backtesting_forecaster_multiseries,\n",
    "    bayesian_search_forecaster_multiseries\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, the data is stored in **long-format DataFrame**. The `series_id` column identifies the time series each observation belongs to, the `timestamp` column contains the corresponding dates, and the `value` column holds the observed values for each series. Each time series may have a different length.\n",
    "\n",
    "The exogenous variables are stored in a separate **long-format DataFrame**. As with the main series, the `series_id` column indicates the associated time series, the `timestamp` column records the dates, and the remaining columns contain the values of the exogenous variables at each timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1012.500694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>1158.500099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>983.000099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id  timestamp        value\n",
       "0   id_1000 2016-01-01  1012.500694\n",
       "1   id_1000 2016-01-02  1158.500099\n",
       "2   id_1000 2016-01-03   983.000099"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>series_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sin_day_of_week</th>\n",
       "      <th>cos_day_of_week</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>6.416639</td>\n",
       "      <td>4.040115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>-0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>6.366474</td>\n",
       "      <td>4.530395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>-0.781831</td>\n",
       "      <td>0.623490</td>\n",
       "      <td>6.555272</td>\n",
       "      <td>3.273064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  series_id  timestamp  sin_day_of_week  cos_day_of_week  air_temperature  \\\n",
       "0   id_1000 2016-01-01        -0.433884        -0.900969         6.416639   \n",
       "1   id_1000 2016-01-02        -0.974928        -0.222521         6.366474   \n",
       "2   id_1000 2016-01-03        -0.781831         0.623490         6.555272   \n",
       "\n",
       "   wind_speed  \n",
       "0    4.040115  \n",
       "1    4.530395  \n",
       "2    3.273064  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load time series of multiple lengths and exogenous variables\n",
    "# ==============================================================================\n",
    "series = pd.read_csv(\n",
    "    'https://raw.githubusercontent.com/skforecast/skforecast-datasets/main/data/demo_multi_series.csv'\n",
    ")\n",
    "exog = pd.read_csv(\n",
    "    'https://raw.githubusercontent.com/skforecast/skforecast-datasets/main/data/demo_multi_series_exog.csv'\n",
    ")\n",
    "\n",
    "series['timestamp'] = pd.to_datetime(series['timestamp'])\n",
    "exog['timestamp'] = pd.to_datetime(exog['timestamp'])\n",
    "\n",
    "display(series.head(3))\n",
    "print(\"\")\n",
    "display(exog.head(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of performance, using a `dict` is more efficient than a `pandas DataFrame`, either wide or long format, especially for larger datasets. This is because dictionaries enable faster access and manipulation of individual time series, without the structural overhead associated with DataFrames.\n",
    "\n",
    "To convert the input data, the <code>[reshape_series_long_to_dict](../api/preprocessing.html#skforecast.preprocessing.preprocessing.reshape_series_long_to_dict)</code> function can be used. It takes a **long-format pandas DataFrame** and returns a **dictionary** where each key corresponds to a series ID and each value is a `pandas Series` representing that time series.\n",
    "\n",
    "Similarly, exogenous variables can be transformed into a dictionary using the <code>[reshape_exog_long_to_dict](../api/preprocessing.html#skforecast.preprocessing.preprocessing.reshape_exog_long_to_dict)</code> function. This function also takes a **long-format pandas DataFrame** as input and returns a **dictionary** in which the keys are the series IDs and the values are the corresponding exogenous variables, represented as `pandas DataFrames`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭──────────────────────────────── MissingValuesWarning ────────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Series 'id_1003' is incomplete. NaNs have been introduced after setting the          <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> frequency.                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.MissingValuesWarning                                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\preproc <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> essing\\preprocessing.py:512                                                          <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m MissingValuesWarning \u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Series 'id_1003' is incomplete. NaNs have been introduced after setting the          \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m frequency.                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.MissingValuesWarning                                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\preproc \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m essing\\preprocessing.py:512                                                          \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transform series and exog to dictionaries\n",
    "# ==============================================================================\n",
    "series_dict = reshape_series_long_to_dict(\n",
    "    data      = series,\n",
    "    series_id = 'series_id',\n",
    "    index     = 'timestamp',\n",
    "    values    = 'value',\n",
    "    freq      = 'D'\n",
    ")\n",
    "\n",
    "exog_dict = reshape_exog_long_to_dict(\n",
    "    data      = exog,\n",
    "    series_id = 'series_id',\n",
    "    index     = 'timestamp',\n",
    "    freq      = 'D'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some exogenous variables are intentionally omitted for series 1 and 3 to illustrate that each series can use a different set of exogenous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop some exogenous variables for series 'id_1000' and 'id_1003'\n",
    "# ==============================================================================\n",
    "exog_dict['id_1000'] = exog_dict['id_1000'].drop(columns=['air_temperature', 'wind_speed'])\n",
    "exog_dict['id_1003'] = exog_dict['id_1003'].drop(columns=['cos_day_of_week'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition data in train and test\n",
    "# ==============================================================================\n",
    "end_train = '2016-07-31 23:59:00'\n",
    "series_dict_train = {k: v.loc[: end_train,] for k, v in series_dict.items()}\n",
    "exog_dict_train   = {k: v.loc[: end_train,] for k, v in exog_dict.items()}\n",
    "series_dict_test  = {k: v.loc[end_train:,] for k, v in series_dict.items()}\n",
    "exog_dict_test    = {k: v.loc[end_train:,] for k, v in exog_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot series\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "fig, axs = plt.subplots(5, 1, figsize=(8, 4), sharex=True)\n",
    "\n",
    "for i, s in enumerate(series_dict.values()):\n",
    "    axs[i].plot(s, label=s.name, color=colors[i])\n",
    "    axs[i].legend(loc='upper right', fontsize=8)\n",
    "    axs[i].tick_params(axis='both', labelsize=8)\n",
    "    axs[i].axvline(pd.to_datetime(end_train), color='white', linestyle='--', linewidth=1)  # End train\n",
    "\n",
    "fig.suptitle('Series in `series_dict`', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_1000:\n",
      "\tTrain: len=213, 2016-01-01 00:00:00 --- 2016-07-31 00:00:00\n",
      "\tTest : len=153, 2016-08-01 00:00:00 --- 2016-12-31 00:00:00\n",
      "id_1001:\n",
      "\tTrain: len=30, 2016-07-02 00:00:00 --- 2016-07-31 00:00:00\n",
      "\tTest : len=153, 2016-08-01 00:00:00 --- 2016-12-31 00:00:00\n",
      "id_1002:\n",
      "\tTrain: len=183, 2016-01-01 00:00:00 --- 2016-07-01 00:00:00\n",
      "\tTest : len=0\n",
      "id_1003:\n",
      "\tTrain: len=213, 2016-01-01 00:00:00 --- 2016-07-31 00:00:00\n",
      "\tTest : len=153, 2016-08-01 00:00:00 --- 2016-12-31 00:00:00\n",
      "id_1004:\n",
      "\tTrain: len=91, 2016-05-02 00:00:00 --- 2016-07-31 00:00:00\n",
      "\tTest : len=31, 2016-08-01 00:00:00 --- 2016-08-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Description of each partition\n",
    "# ==============================================================================\n",
    "for k in series_dict.keys():\n",
    "    print(f\"{k}:\")\n",
    "    try:\n",
    "        print(\n",
    "            f\"\\tTrain: len={len(series_dict_train[k])}, {series_dict_train[k].index[0]}\"\n",
    "            f\" --- {series_dict_train[k].index[-1]}\"\n",
    "        )\n",
    "    except IndexError:\n",
    "        print(\"\\tTrain: len=0\")\n",
    "    try:\n",
    "        print(\n",
    "            f\"\\tTest : len={len(series_dict_test[k])}, {series_dict_test[k].index[0]}\"\n",
    "            f\" --- {series_dict_test[k].index[-1]}\"\n",
    "        )\n",
    "    except IndexError:\n",
    "        print(\"\\tTest : len=0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_1000:\n",
      "\t['sin_day_of_week', 'cos_day_of_week']\n",
      "id_1001:\n",
      "\t['sin_day_of_week', 'cos_day_of_week', 'air_temperature', 'wind_speed']\n",
      "id_1002:\n",
      "\t['sin_day_of_week', 'cos_day_of_week', 'air_temperature', 'wind_speed']\n",
      "id_1003:\n",
      "\t['sin_day_of_week', 'air_temperature', 'wind_speed']\n",
      "id_1004:\n",
      "\t['sin_day_of_week', 'cos_day_of_week', 'air_temperature', 'wind_speed']\n"
     ]
    }
   ],
   "source": [
    "# Exogenous variables for each series\n",
    "# ==============================================================================\n",
    "for k in series_dict.keys():\n",
    "    print(f\"{k}:\")\n",
    "    try:\n",
    "        print(f\"\\t{exog_dict[k].columns.to_list()}\")\n",
    "    except IndexError:\n",
    "        print(\"\\tNo exogenous variables\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and predict\n",
    "\n",
    "The `fit` method is used to train the model, it is passed the dictionary of series and the dictionary of exogenous variables where the keys of each dictionary are the names of the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </style>\n",
       "    \n",
       "        <div class=\"container-04a8fddd91984093988f489e3ee4c7d7\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursiveMultiSeries</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_7', 'roll_mean_14']</li>\n",
       "                    <li><strong>Window size:</strong> 14</li>\n",
       "                    <li><strong>Series encoding:</strong> ordinal</li>\n",
       "                    <li><strong>Exogenous included:</strong> True</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Series weights:</strong> None</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:20:57</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:20:59</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    sin_day_of_week, cos_day_of_week, air_temperature, wind_speed\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for series:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Series names (levels):</strong> id_1000, id_1001, id_1002, id_1003, id_1004</li>\n",
       "                    <li><strong>Training range:</strong> 'id_1000': ['2016-01-01', '2016-07-31'], 'id_1001': ['2016-07-02', '2016-07-31'], 'id_1002': ['2016-01-01', '2016-07-01'], 'id_1003': ['2016-01-01', '2016-07-31'], 'id_1004': ['2016-05-02', '2016-07-31']</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <Day></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursivemultiseries.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/independent-multi-time-series-forecasting.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "============================== \n",
       "ForecasterRecursiveMultiSeries \n",
       "============================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14] \n",
       "Window features: ['roll_mean_7', 'roll_mean_14'] \n",
       "Window size: 14 \n",
       "Series encoding: ordinal \n",
       "Series names (levels): id_1000, id_1001, id_1002, id_1003, id_1004 \n",
       "Exogenous included: True \n",
       "Exogenous names: \n",
       "    sin_day_of_week, cos_day_of_week, air_temperature, wind_speed \n",
       "Transformer for series: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Series weights: None \n",
       "Differentiation order: None \n",
       "Training range: \n",
       "    'id_1000': ['2016-01-01', '2016-07-31'], 'id_1001': ['2016-07-02',\n",
       "    '2016-07-31'], 'id_1002': ['2016-01-01', '2016-07-01'], 'id_1003':\n",
       "    ['2016-01-01', '2016-07-31'], 'id_1004': ['2016-05-02', '2016-07-31'] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <Day> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:20:57 \n",
       "Last fit date: 2025-11-26 15:20:59 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit forecaster\n",
    "# ==============================================================================\n",
    "window_features = RollingFeatures(stats=['mean', 'mean'], window_sizes=[7, 14])\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "                 estimator          = LGBMRegressor(random_state=123, verbose=-1, max_depth=5), \n",
    "                 lags               = 14, \n",
    "                 window_features    = window_features,\n",
    "                 encoding           = \"ordinal\", \n",
    "                 dropna_from_series = False\n",
    "             )\n",
    "\n",
    "forecaster.fit(series=series_dict_train, exog=exog_dict_train, suppress_warnings=True)\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only series whose **last window** ends at the same datetime index can be predicted simultaneously. If `levels=None`, any series that does not extend to the maximum datetime index is excluded from the prediction.\n",
    "\n",
    "In this example, series `'id_1002'` is excluded because its last available observation occurs before the latest index shared by the other series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'id_1000': Timestamp('2016-07-31 00:00:00'),\n",
      " 'id_1001': Timestamp('2016-07-31 00:00:00'),\n",
      " 'id_1002': Timestamp('2016-07-01 00:00:00'),\n",
      " 'id_1003': Timestamp('2016-07-31 00:00:00'),\n",
      " 'id_1004': Timestamp('2016-07-31 00:00:00')}\n"
     ]
    }
   ],
   "source": [
    "# Internal last window maximum index\n",
    "# ==============================================================================\n",
    "pprint(\n",
    "    {k: v.index[-1] for k, v in forecaster.last_window_.items() if v is not None}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>1453.312971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>2849.347882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>2706.851726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1004</td>\n",
       "      <td>7496.555367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>1440.763196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>2947.579536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>2310.075968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>id_1004</td>\n",
       "      <td>8685.425990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-03</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>1410.151437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              level         pred\n",
       "2016-08-01  id_1000  1453.312971\n",
       "2016-08-01  id_1001  2849.347882\n",
       "2016-08-01  id_1003  2706.851726\n",
       "2016-08-01  id_1004  7496.555367\n",
       "2016-08-02  id_1000  1440.763196\n",
       "2016-08-02  id_1001  2947.579536\n",
       "2016-08-02  id_1003  2310.075968\n",
       "2016-08-02  id_1004  8685.425990\n",
       "2016-08-03  id_1000  1410.151437"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict\n",
    "# ==============================================================================\n",
    "predictions = forecaster.predict(steps=5, exog=exog_dict_test, suppress_warnings=True)\n",
    "predictions.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing values in the series\n",
    "\n",
    "When working with time series of different lengths, it is common for some series to contain missing values. Since not all estimators can handle missing data, the `dropna_from_series` argument controls how missing values in the predictors are treated when building the training matrices.\n",
    "\n",
    "- If set to `False` (default), `NaN` values in `X_train` are retained, and a warning is issued.\n",
    "\n",
    "- If set to `True`, rows with `NaN` values in `X_train` are dropped, and the corresponding rows in y_train are removed as well. A warning is also issued.\n",
    "\n",
    "Regardless of this setting, any `NaN` values in `y_train` are always dropped, along with the corresponding rows in `X_train`, as the target variable cannot contain missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample data with interspersed NaNs\n",
    "# ==============================================================================\n",
    "series_dict_nan = {\n",
    "    'id_1000': series_dict['id_1000'].copy(),\n",
    "    'id_1003': series_dict['id_1003'].copy()\n",
    "}\n",
    "\n",
    "# Create NaNs\n",
    "series_dict_nan['id_1000'].loc['2016-03-01':'2016-04-01',] = np.nan\n",
    "series_dict_nan['id_1000'].loc['2016-05-01':'2016-05-07',] = np.nan\n",
    "series_dict_nan['id_1003'].loc['2016-07-01',] = np.nan\n",
    "\n",
    "# Plot series\n",
    "# ==============================================================================\n",
    "fig, axs = plt.subplots(2, 1, figsize=(8, 2.5), sharex=True)\n",
    "\n",
    "for i, s in enumerate(series_dict_nan.values()):\n",
    "    axs[i].plot(s, label=s.name, color=colors[i])\n",
    "    axs[i].legend(loc='upper right', fontsize=8)\n",
    "    axs[i].tick_params(axis='both', labelsize=8)\n",
    "    axs[i].axvline(pd.to_datetime(end_train), color='white', linestyle='--', linewidth=1)  # End train\n",
    "\n",
    "fig.suptitle('Series in `series_dict_nan`', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When `dropna_from_series=False,` any `NaN` values in `X_train` are retained, and a warning is issued. This setting is useful when the user intends to preserve missing values in the input series and use a estimator that can handle them natively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭──────────────────────────────── MissingValuesWarning ────────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> NaNs detected in `y_train`. They have been dropped because the target variable       <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> cannot have NaN values. Same rows have been dropped from `X_train` to maintain       <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> alignment. This is caused by series with interspersed NaNs.                          <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.MissingValuesWarning                                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> ve\\_forecaster_recursive_multiseries.py:1265                                         <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m MissingValuesWarning \u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m NaNs detected in `y_train`. They have been dropped because the target variable       \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m cannot have NaN values. Same rows have been dropped from `X_train` to maintain       \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m alignment. This is caused by series with interspersed NaNs.                          \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.MissingValuesWarning                                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m ve\\_forecaster_recursive_multiseries.py:1265                                         \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭──────────────────────────────── MissingValuesWarning ────────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> NaNs detected in `X_train`. Some estimators do not allow NaN values during training. <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> If you want to drop them, set `forecaster.dropna_from_series = True`.                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.MissingValuesWarning                                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> ve\\_forecaster_recursive_multiseries.py:1287                                         <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m MissingValuesWarning \u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m NaNs detected in `X_train`. Some estimators do not allow NaN values during training. \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m If you want to drop them, set `forecaster.dropna_from_series = True`.                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.MissingValuesWarning                                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m ve\\_forecaster_recursive_multiseries.py:1287                                         \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>_level_skforecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>983.000099</td>\n",
       "      <td>1158.500099</td>\n",
       "      <td>1012.500694</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1675.750496</td>\n",
       "      <td>983.000099</td>\n",
       "      <td>1158.500099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1586.250694</td>\n",
       "      <td>1675.750496</td>\n",
       "      <td>983.000099</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  lag_1        lag_2        lag_3  _level_skforecast\n",
       "2016-01-04   983.000099  1158.500099  1012.500694                  0\n",
       "2016-01-05  1675.750496   983.000099  1158.500099                  0\n",
       "2016-01-06  1586.250694  1675.750496   983.000099                  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations per series:\n",
      "_level_skforecast\n",
      "0    324\n",
      "1    216\n",
      "Name: count, dtype: int64\n",
      "\n",
      "NaNs per series:\n",
      "lag_1                 5\n",
      "lag_2                 9\n",
      "lag_3                13\n",
      "_level_skforecast     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create Matrices, dropna_from_series = False\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "                 estimator          = LGBMRegressor(random_state=123, verbose=-1, max_depth=5), \n",
    "                 lags               = 3, \n",
    "                 encoding           = \"ordinal\", \n",
    "                 dropna_from_series = False\n",
    "             )\n",
    "\n",
    "X, y = forecaster.create_train_X_y(series=series_dict_nan)\n",
    "\n",
    "display(X.head(3))\n",
    "print(\"Observations per series:\")\n",
    "print(X['_level_skforecast'].value_counts())\n",
    "print(\"\")\n",
    "print(\"NaNs per series:\")\n",
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When `dropna_from_series=True`, any `NaN` values in `X_train` are removed, and a warning is issued. This option is useful when the chosen estimator cannot handle missing values and requires a fully populated training matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭──────────────────────────────── MissingValuesWarning ────────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> NaNs detected in `y_train`. They have been dropped because the target variable       <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> cannot have NaN values. Same rows have been dropped from `X_train` to maintain       <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> alignment. This is caused by series with interspersed NaNs.                          <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.MissingValuesWarning                                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> ve\\_forecaster_recursive_multiseries.py:1265                                         <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m MissingValuesWarning \u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m NaNs detected in `y_train`. They have been dropped because the target variable       \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m cannot have NaN values. Same rows have been dropped from `X_train` to maintain       \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m alignment. This is caused by series with interspersed NaNs.                          \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.MissingValuesWarning                                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m ve\\_forecaster_recursive_multiseries.py:1265                                         \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╭──────────────────────────────── MissingValuesWarning ────────────────────────────────╮</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> NaNs detected in `X_train`. They have been dropped. If you want to keep them, set    <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> `forecaster.dropna_from_series = False`. Same rows have been removed from `y_train`  <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> to maintain alignment. This caused by series with interspersed NaNs.                 <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>                                                                                      <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Category : skforecast.exceptions.MissingValuesWarning                                <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Location :                                                                           <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> ve\\_forecaster_recursive_multiseries.py:1278                                         <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span> Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            <span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">│</span>\n",
       "<span style=\"color: #ffaf00; text-decoration-color: #ffaf00\">╰──────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[38;5;214m╭─\u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m MissingValuesWarning \u001b[0m\u001b[38;5;214m───────────────────────────────\u001b[0m\u001b[38;5;214m─╮\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m NaNs detected in `X_train`. They have been dropped. If you want to keep them, set    \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m `forecaster.dropna_from_series = False`. Same rows have been removed from `y_train`  \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m to maintain alignment. This caused by series with interspersed NaNs.                 \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m                                                                                      \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Category : skforecast.exceptions.MissingValuesWarning                                \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Location :                                                                           \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m c:\\Users\\jaesc2\\Miniconda3\\envs\\skforecast_py12\\Lib\\site-packages\\skforecast\\recursi \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m ve\\_forecaster_recursive_multiseries.py:1278                                         \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m│\u001b[0m Suppress : warnings.simplefilter('ignore', category=MissingValuesWarning)            \u001b[38;5;214m│\u001b[0m\n",
       "\u001b[38;5;214m╰──────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>_level_skforecast</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>983.000099</td>\n",
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      ],
      "text/plain": [
       "                  lag_1        lag_2        lag_3  _level_skforecast\n",
       "2016-01-04   983.000099  1158.500099  1012.500694                  0\n",
       "2016-01-05  1675.750496   983.000099  1158.500099                  0\n",
       "2016-01-06  1586.250694  1675.750496   983.000099                  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Observations per series:\n",
      "_level_skforecast\n",
      "0    318\n",
      "1    207\n",
      "Name: count, dtype: int64\n",
      "\n",
      "NaNs per series:\n",
      "lag_1                0\n",
      "lag_2                0\n",
      "lag_3                0\n",
      "_level_skforecast    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create Matrices, dropna_from_series = False\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "                 estimator          = LGBMRegressor(random_state=123, verbose=-1, max_depth=5), \n",
    "                 lags               = 3, \n",
    "                 encoding           = \"ordinal\", \n",
    "                 dropna_from_series = True\n",
    "             )\n",
    "\n",
    "X, y = forecaster.create_train_X_y(series=series_dict_nan)\n",
    "\n",
    "display(X.head(3))\n",
    "print(\"Observations per series:\")\n",
    "print(X['_level_skforecast'].value_counts())\n",
    "print(\"\")\n",
    "print(\"NaNs per series:\")\n",
    "print(X.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the training process, the warnings can be suppressed by setting `suppress_warnings = True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "    \n",
       "        <div class=\"container-4e96e7d0fb534d6bace54583cf8300d7\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursiveMultiSeries</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [1 2 3]</li>\n",
       "                    <li><strong>Window features:</strong> None</li>\n",
       "                    <li><strong>Window size:</strong> 3</li>\n",
       "                    <li><strong>Series encoding:</strong> ordinal</li>\n",
       "                    <li><strong>Exogenous included:</strong> False</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Series weights:</strong> None</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 15:20:59</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 15:20:59</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    None\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for series:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Series names (levels):</strong> id_1000, id_1003</li>\n",
       "                    <li><strong>Training range:</strong> 'id_1000': ['2016-01-01', '2016-12-31'], 'id_1003': ['2016-01-01', '2016-12-31']</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <Day></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursivemultiseries.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/independent-multi-time-series-forecasting.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "============================== \n",
       "ForecasterRecursiveMultiSeries \n",
       "============================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [1 2 3] \n",
       "Window features: None \n",
       "Window size: 3 \n",
       "Series encoding: ordinal \n",
       "Series names (levels): id_1000, id_1003 \n",
       "Exogenous included: False \n",
       "Exogenous names: None \n",
       "Transformer for series: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Series weights: None \n",
       "Differentiation order: None \n",
       "Training range: \n",
       "    'id_1000': ['2016-01-01', '2016-12-31'], 'id_1003': ['2016-01-01', '2016-12-31'] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <Day> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 5,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 123, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0,\n",
       "    'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 15:20:59 \n",
       "Last fit date: 2025-11-26 15:20:59 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Suppress warnings during fit method\n",
    "# ==============================================================================\n",
    "forecaster.fit(series=series_dict_nan, suppress_warnings=True)\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting\n",
    "\n",
    "As with the `predict` method, the `levels` parameter must be specified when performing [backtesting](../user_guides/backtesting.html). When backtesting time series of varying lengths, two important behaviors should be considered:\n",
    "\n",
    "+ **Handling missing values in the series:** Backtesting only produces predictions for the date-times that are present in each individual series. If there are gaps in the validation or test sets, no predictions are generated for those missing periods. This ensures that evaluation metrics are not distorted by the absence of true values.\n",
    "\n",
    "+ **Size of the training set:** If the `initial_train_size` parameter is specified as an `integer`, it is applied relative to the earliest timestamp across all series. Consequently, series that start later may have a shorter effective training window, or even no available training data during the initial folds. Alternatively, `initial_train_size` can be specified as a datetime, which can make the training window easier to interpret and align with specific points in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?it/s]"
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    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>levels</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>167.502214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>1103.313887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_1002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>280.492603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id_1004</td>\n",
       "      <td>711.078359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>average</td>\n",
       "      <td>565.596766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>weighted_average</td>\n",
       "      <td>572.944127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pooling</td>\n",
       "      <td>572.944127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             levels  mean_absolute_error\n",
       "0           id_1000           167.502214\n",
       "1           id_1001          1103.313887\n",
       "2           id_1002                  NaN\n",
       "3           id_1003           280.492603\n",
       "4           id_1004           711.078359\n",
       "5           average           565.596766\n",
       "6  weighted_average           572.944127\n",
       "7           pooling           572.944127"
      ]
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level</th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1453.312971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>0</td>\n",
       "      <td>2849.347882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>0</td>\n",
       "      <td>2706.851726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>id_1004</td>\n",
       "      <td>0</td>\n",
       "      <td>7496.555367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1440.763196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>6</td>\n",
       "      <td>1132.535774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>6</td>\n",
       "      <td>2089.261345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>id_1000</td>\n",
       "      <td>6</td>\n",
       "      <td>1393.128313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>id_1001</td>\n",
       "      <td>6</td>\n",
       "      <td>1106.034061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-31</th>\n",
       "      <td>id_1003</td>\n",
       "      <td>6</td>\n",
       "      <td>2064.475030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>507 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              level  fold         pred\n",
       "2016-08-01  id_1000     0  1453.312971\n",
       "2016-08-01  id_1001     0  2849.347882\n",
       "2016-08-01  id_1003     0  2706.851726\n",
       "2016-08-01  id_1004     0  7496.555367\n",
       "2016-08-02  id_1000     0  1440.763196\n",
       "...             ...   ...          ...\n",
       "2016-12-30  id_1001     6  1132.535774\n",
       "2016-12-30  id_1003     6  2089.261345\n",
       "2016-12-31  id_1000     6  1393.128313\n",
       "2016-12-31  id_1001     6  1106.034061\n",
       "2016-12-31  id_1003     6  2064.475030\n",
       "\n",
       "[507 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "                 estimator          = LGBMRegressor(random_state=123, verbose=-1, max_depth=5), \n",
    "                 lags               = 14, \n",
    "                 window_features    = RollingFeatures(stats=['mean', 'mean'], window_sizes=[7, 14]),\n",
    "                 encoding           = \"ordinal\", \n",
    "                 dropna_from_series = False\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 24,\n",
    "         initial_train_size = \"2016-07-31 23:59:00\",\n",
    "     )\n",
    "\n",
    "metrics_levels, backtest_predictions = backtesting_forecaster_multiseries(\n",
    "    forecaster            = forecaster,\n",
    "    series                = series_dict,\n",
    "    exog                  = exog_dict,\n",
    "    cv                    = cv,\n",
    "    levels                = None,\n",
    "    metric                = \"mean_absolute_error\",\n",
    "    add_aggregated_metric = True,\n",
    "    suppress_warnings     = True\n",
    ")\n",
    "\n",
    "display(metrics_levels)\n",
    "backtest_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if a series has no observations in the test set, the backtesting process will not generate any predictions for that series, and the corresponding metric will be `NaN`. This is the case for series `'id_1002'` in the current example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot backtesting predictions\n",
    "# ==============================================================================\n",
    "fig, axs = plt.subplots(5, 1, figsize=(8, 4), sharex=True)\n",
    "\n",
    "for i, s in enumerate(series_dict.keys()):\n",
    "    axs[i].plot(series_dict[s], label=series_dict[s].name, color=colors[i])\n",
    "    axs[i].axvline(pd.to_datetime(end_train), color='white', linestyle='--', linewidth=1)  # End train\n",
    "    try:\n",
    "        axs[i].plot(backtest_predictions[s], label='prediction', color=\"white\")\n",
    "    except KeyError:\n",
    "        pass\n",
    "    axs[i].legend(loc='upper right', fontsize=8)\n",
    "    axs[i].tick_params(axis='both', labelsize=8)\n",
    "\n",
    "fig.suptitle('Backtest Predictions', fontsize=15)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter search and lags selection\n",
    "\n",
    "Hyperparameter tuning consists of systematically evaluating combinations of hyperparameters (including lags) to find the configuration that yields the best predictive performance. The **skforecast** library supports several tuning strategies: **grid search**, **random search**, and **Bayesian search**. These strategies can be used with either [backtesting](../user_guides/backtesting.html) or [one-step-ahead validation](../user_guides/hyperparameter-tuning-and-lags-selection.html#one-step-ahead-validation) to determine the optimal parameter set for a given forecasting task.\n",
    "\n",
    "The functions `grid_search_forecaster_multiseries`, `random_search_forecaster_multiseries` and `bayesian_search_forecaster_multiseries` from the `model_selection` module allow for **lags and hyperparameter optimization**.\n",
    "\n",
    "The `levels` argument is crucial in these functions, as it specifies **which time series are considered** during the optimization:\n",
    "\n",
    "+ If `levels` is a `list`, the function searches for the configuration that minimizes the aggregated error across the selected series.\n",
    "\n",
    "+ If `levels = None`, all available series are used in the optimization.\n",
    "\n",
    "+ If `levels = 'item_1'` (equivalent to `levels = ['item_1']`), the optimization is based solely on the error of that specific series, and the returned metric corresponds to it.\n",
    "\n",
    "When optimizing across multiple series, the resulting metrics must be **aggregated**. The available methods are:\n",
    "\n",
    "+ `'average'`, the average (arithmetic mean) of all series (`levels`).\n",
    "\n",
    "+ `'weighted_average'`, the average of the metrics weighted by the number of predicted values of each series (`levels`).\n",
    "\n",
    "+ `'pooling'`, the values of all series (`levels`) are pooled and then the metric is calculated.\n",
    "\n",
    "If a `list` of aggregation methods is provided to the `aggregate_metric` argument, all are computed, but only the **first method** is used to select the **best configuration**.\n",
    "\n",
    "The following example demonstrates how to use `grid_search_forecaster_multiseries` to find the best lags and hyperparameters for all series (all `levels`) using `'weighted_average'` as the aggregation strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5b5b93a476c46d2b9eb32d2c9789d7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`Forecaster` refitted using the best-found lags and parameters, and the whole data set: \n",
      "  Lags: [ 1  2  3  4  5  6  7  8  9 10 11 12 13 14] \n",
      "  Parameters: {'min_samples_leaf': 29, 'max_depth': 5}\n",
      "  Backtesting metric: 531.5731935466865\n",
      "  Levels: ['id_1000', 'id_1001', 'id_1002', 'id_1003', 'id_1004']\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>levels</th>\n",
       "      <th>lags</th>\n",
       "      <th>params</th>\n",
       "      <th>mean_absolute_error__weighted_average</th>\n",
       "      <th>mean_absolute_error__average</th>\n",
       "      <th>mean_absolute_error__pooling</th>\n",
       "      <th>min_samples_leaf</th>\n",
       "      <th>max_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[id_1000, id_1001, id_1002, id_1003, id_1004]</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</td>\n",
       "      <td>{'min_samples_leaf': 29, 'max_depth': 5}</td>\n",
       "      <td>531.573194</td>\n",
       "      <td>510.885119</td>\n",
       "      <td>510.885119</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[id_1000, id_1001, id_1002, id_1003, id_1004]</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</td>\n",
       "      <td>{'min_samples_leaf': 11, 'max_depth': 5}</td>\n",
       "      <td>542.700963</td>\n",
       "      <td>557.169748</td>\n",
       "      <td>557.169748</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[id_1000, id_1001, id_1002, id_1003, id_1004]</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]</td>\n",
       "      <td>{'min_samples_leaf': 18, 'max_depth': 4}</td>\n",
       "      <td>572.011630</td>\n",
       "      <td>559.198862</td>\n",
       "      <td>559.198862</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[id_1000, id_1001, id_1002, id_1003, id_1004]</td>\n",
       "      <td>[1, 2, 3, 4, 5, 6, 7]</td>\n",
       "      <td>{'min_samples_leaf': 30, 'max_depth': 6}</td>\n",
       "      <td>591.817179</td>\n",
       "      <td>595.234722</td>\n",
       "      <td>595.234722</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          levels  \\\n",
       "0  [id_1000, id_1001, id_1002, id_1003, id_1004]   \n",
       "1  [id_1000, id_1001, id_1002, id_1003, id_1004]   \n",
       "2  [id_1000, id_1001, id_1002, id_1003, id_1004]   \n",
       "3  [id_1000, id_1001, id_1002, id_1003, id_1004]   \n",
       "\n",
       "                                              lags  \\\n",
       "0  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]   \n",
       "1  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]   \n",
       "2  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]   \n",
       "3                            [1, 2, 3, 4, 5, 6, 7]   \n",
       "\n",
       "                                     params  \\\n",
       "0  {'min_samples_leaf': 29, 'max_depth': 5}   \n",
       "1  {'min_samples_leaf': 11, 'max_depth': 5}   \n",
       "2  {'min_samples_leaf': 18, 'max_depth': 4}   \n",
       "3  {'min_samples_leaf': 30, 'max_depth': 6}   \n",
       "\n",
       "   mean_absolute_error__weighted_average  mean_absolute_error__average  \\\n",
       "0                             531.573194                    510.885119   \n",
       "1                             542.700963                    557.169748   \n",
       "2                             572.011630                    559.198862   \n",
       "3                             591.817179                    595.234722   \n",
       "\n",
       "   mean_absolute_error__pooling  min_samples_leaf  max_depth  \n",
       "0                    510.885119                29          5  \n",
       "1                    557.169748                11          5  \n",
       "2                    559.198862                18          4  \n",
       "3                    595.234722                30          6  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bayesian search hyperparameters and lags with Optuna\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursiveMultiSeries(\n",
    "                 estimator          = LGBMRegressor(random_state=123, verbose=-1), \n",
    "                 lags               = 14, \n",
    "                 window_features    = RollingFeatures(stats=['mean', 'mean'], window_sizes=[7, 14]),\n",
    "                 encoding           = \"ordinal\", \n",
    "                 dropna_from_series = False\n",
    "             )\n",
    "\n",
    "# Search space\n",
    "def search_space(trial):\n",
    "    search_space  = {\n",
    "        'lags'            : trial.suggest_categorical('lags', [7, 14]),\n",
    "        'min_samples_leaf': trial.suggest_int('min_samples_leaf', 10, 30),\n",
    "        'max_depth'       : trial.suggest_int('max_depth', 3, 7)\n",
    "    }\n",
    "\n",
    "    return search_space\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 24,\n",
    "         initial_train_size = \"2016-07-31 23:59:00\",\n",
    "     )\n",
    "\n",
    "results, best_trial = bayesian_search_forecaster_multiseries(\n",
    "    forecaster        = forecaster,\n",
    "    series            = series_dict,\n",
    "    exog              = exog_dict,\n",
    "    search_space      = search_space,\n",
    "    cv                = cv,\n",
    "    levels            = None,\n",
    "    metric            = 'mean_absolute_error',\n",
    "    n_trials          = 10,\n",
    "    suppress_warnings = True\n",
    ")\n",
    "\n",
    "results.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "Starting with version <code>0.13.0</code>, the <code>ForecasterRecursiveMultiSeries</code> class can use <code>encoding='ordinal_category'</code> for encoding time series identifiers. This approach creates a new column (<i>_level_skforecast</i>) of type pandas <code>category</code>. Consequently, the estimators must be able to handle categorical variables. If the estimators do not support categorical variables, the user should set the encoding to <code>'ordinal'</code> or <code>'onehot'</code> for compatibility.\n",
    "\n",
    "<p>Some examples of estimators that support categorical variables and how to enable them are:</p>\n",
    "\n",
    "<strong>HistGradientBoostingRegressor</strong> \n",
    "\n",
    "```python\n",
    "HistGradientBoostingRegressor(categorical_features=\"from_dtype\")\n",
    "```\n",
    "\n",
    "<strong>LightGBM</strong>\n",
    "\n",
    "<code>LGBMRegressor</code> does not allow configuration of categorical features during initialization, but rather in its <code>fit</code> method. Therefore, use Forecaster' argument <code>fit_kwargs = {'categorical_feature':'auto'}</code>. This is the default behavior of <code>LGBMRegressor</code> if no indication is given.\n",
    "\n",
    "<strong>XGBoost</strong>\n",
    "```python\n",
    "XGBRegressor(enable_categorical=True)\n",
    "```\n",
    "\n",
    "</div>"
   ]
  }
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